3/3 By leveraging generative AI, our framework significantly improves downstream tasks compared to traditional methods, including better cell type annotation and patient survival prediction. 📉✅ Authors: Minxing Pang, Tarun Kanti Roy, Xiaodong Wu, and Kai Tan. #GenerativeAI #SpatialOmics #Pathology

2/3 To power this, we created PRISM-12M: a massive dataset of 12M+ patches across 14 tissue types. 📂 TissueCraftAI uses this to generate multi-modal spatial transcriptomics & proteomics maps from simple natural language prompts, covering both human and mouse tissues. 🐭🧍‍♂️

1/3 Excited to share our latest preprint: TissueCraftAI! 🚀 It's the FIRST 1B-parameter spatial omics model built on ControlNet & Stable Diffusion. 🧬🤖 Moving beyond CNNs, it enables query-driven prediction of molecular maps directly from histology. 🔗 www.biorxiv.org/content/10.6...